Muscle analysis of hand and forearm during tapping using surface electromyography

Surface electromyography (sEMG) is one of the promising sensors to handle biological information especially for user interfaces with low hardware cost. However, sEMG signals are noisy and the sensor position affects to the signal-to-noise ratio (SNR). Assuming sEMG sensors to be inputs of wearable controllers of some devices (head-mounted displays for instance), we examined the SNR of sEMG signals of a forearm muscle (flexor digitorum superficialis) and two hand muscles (dorsal interossei and lumbrical) when tapped on a desk by the index finger. As a result, the SNR of sEMG signals of hands were higher than the one of the signals of forearms. The result shows hands are more suitable than forearms for wearable controllers with tapping-gesture using sEMG. Ten subjects participated, and two different forms of tapping gesture by index fingers were adopted in our experiments.